Research On The Analysis Algorithm Of Food Safety Public Opinion Based On Cloud Computing

Food safety is related to the people’s vital interests and it is the prominent problem of the people’s livelihood.With the development of the Internet,people can publish their views and opinions on food safety issues through the Internet anytime and anywhere.One of the hot issues is easy to spread with some one-sided and false reports,misleading the broad masses of the people,and endangering people’s trust in the country and the enterprises.Through the collection of food safety public opinion information on the web,the analysis of food safety public opinion information,the improvement of the accuracy and speed of relevant algorithms,and the combination of cloud computing technology to improve operational efficiency are of great significance to building a stable and harmonious society.Food safety public opinion information is mainly web-based text information,and the main processing method is similar to text processing.This article provides an in-depth understanding of techniques for obtaining food safety public opinion information,text segmentation,feature selection,and text classification.Text categorization is the basis of analysis techniques such as document classification,subject tracking and sentiment orientation in food safety public opinion analysis.This paper focuses on text categorization technology and feature selection techniques,and based on this,has made improvement research.For KNN,when the training sample size is too large,the efficiency is not high.In this paper,through the effective reduction of the training data set,an improved KNN algorithm is proposed and verified on the standard data set.The accuracy and efficiency of the algorithm is improved.Taking into account the mutual information MI as a text feature extraction,using word frequency to calculate the probability,ignoring the distribution of feature words within and between classes,this paper proposes an improved feature selection method N-MD-MI.Experiments on the standard data set and on food safety data set have verified the effectiveness of the improved algorithm.This paper builds a food safety cloud computing environment,designs and implements parallelization of Bayesian classification algorithm and improved mutual information feature selection algorithm.This also conducts experiments to effectively improve the operating efficiency and validates the effectiveness of the food safety cloud platform.